code: def __init__(
      self,
      embedding_shape: Tuple[int, int],
      lstm_size=50,
      embedding_weights=None,
      embedding_trainable=True,
  ) -> None:
      super(Decoder, self).__init__()
      self.lstm_size = lstm_size
      vocab_size, vec_dim = embedding_shape
      weights = (
          None if embedding_weights is None else [embedding_weights]
      )
      self.embedding = tf.keras.layers.Embedding(
          vocab_size,
          vec_dim,
          weights=weights,
          trainable=embedding_trainable,
      )
      self.lstm = tf.keras.layers.LSTM(
          lstm_size, return_sequences=True, return_state=True
      )
      self.dense = tf.keras.layers.Dense(vocab_size)
  
file_path: headliner/headliner/model/basic_model.py
imports: from typing import Tuple
  import tensorflow
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: tf.keras with example: obj = Decoder(embedding_shape, lstm_size, embedding_weights, embedding_trainable)